Fault Detection Mechanism Research Articles

IoT based fault identification in solar photovoltaic systems using an extreme learning machine technique

Due to the shortage of fossil fuel usage, the solar Photovoltaic (PV) energy has increased grownup over the last decade. Most conventional applications of renewable energy are being phased out in order to reduce costs and save the environment. PV plants undergo numerous failures in faults detection and ultimate power developments. These consequences demonstrate in the environmental field and internal components. Even when internal standards are followed, the faults are unavoidable and undetectable. Due to this, the performance of manufacturing plants are not predictable. As a result, a proper fault detection mechanism is required for a PV system to detect faults and avoid energy losses. To address these issues, this research work proposed Internet of Things (IoT) sensor-based fault identification in a solar PV system. The PV panel status is monitored using pressure, light intensity, voltage, and current sensors. These sensor data’s are stored in the cloud for further analysis using a web-based control server. To classify the sensor data, models of Support Vector Machine (SVM), and Extreme Learning Machine (ELM) are utilized. The experimental results indicate that ELM achieves a classification accuracy of 96.32%. Which is higher than SVM and other optimization control techniques. The proposed model uses the IoT cloud to provide real-time monitoring and fault detection in plant environmental and electrical parameters.

Proving SIFA protection of masked redundant circuits

Implementation attacks like side-channel and fault attacks pose a considerable threat to cryptographic devices that are physically accessible by an attacker. As a consequence, devices like smart cards implement corresponding countermeasures like redundant computation and masking. Recently, statistical ineffective fault attacks (SIFA) have been shown to be able to circumvent these classical countermeasure techniques. We present a new approach for verifying the SIFA protection of arbitrary masked implementations in both hardware and software. The proposed method uses Boolean dependency analysis, factorization, and known properties of masked computations to show whether the fault detection mechanism of redundant masked circuits can leak information about the processed secret values. We implemented this new method in a tool called Danira, which can show the SIFA resistance of cryptographic implementations like AES S-Boxes within minutes.

Parabolic tapering piezoelectric rotational energy harvester: Numerical analysis with experimental validation

Inadequate host beam design leads to the inefficiency of any piezoelectric vibration energy harvester. This article presents a parabolic tapering width piezoelectric energy harvester under rotational excitation. The Euler-Bernoulli theory, Hamilton's principle, and Galerkin's approach are used for mathematical modeling to investigate the frequency characteristics and open-circuit voltage response. Finally, experiments are conducted for validation. Compared to earlier reported designs, the proposed harvester produced high voltage-per-total mass (43.618 V/Kg) and performance amplification factor (4.18), besides the advantageous low natural frequency. This research’s findings can be used in real-time health monitoring of rotating machine components and fault detection mechanisms.

Signal-to-Noise Ratio Based Fault Detection and Identification

In this work, we introduce signal-to-noise ratio (SNR) based fault detection and identification mechanisms for a networked control system feedback loop, where the network component is represented by an additive white noise (AWN) channel. The SNR approach is known to be a steady-state analysis and design tool, thus we first introduce a finite time approximation for the estimated AWN channel SNR. We then consider the case of a general linear time-invariant plant model with one unstable pole. The potential faults that we discuss here cover simultaneously the plant model gain and/or the unstable pole. The fault detection is performed relative to the estimated AWN channel SNR. The fault identification is performed using recursive least squares ideas and then further validated with the observed SNR value, when a fault has been previously detected. We show that the proposed SNR-based fault mechanism (fault detection plus fault identification) is capable of processing the proposed faults. We conclude discussing future research based on the contributions exposed in the present work.

Event-Triggered Observer-Based $\mathcal {H}_\infty$ Consensus Control and Fault Detection of Multiagent Systems Under Stochastic False Data Injection Attacks

This paper investigates the event-triggered observer-based security consensus and fault detection problem for nonlinear multi-agent systems (MASs) under external disturbances and stochastic false data injection attacks (FDIAs) over a directed communication network. The randomly occurring FDIAs are modeled by random variables that follow the Bernoulli distribution. An observer-based event-triggered control strategy using only local measurements and information from neighboring agents is developed, where the Zeno behavior of event-triggered mechanism (ETM) is excluded. Interestingly, the observer errors are first regarded as disturbance and then attenuated by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> norm bounds, together with the external disturbances. Meanwhile, it is worth highlighting here that the same information used by the state observers is also adopted to construct residuals with adaptive thresholds, whose aim is to detect faults occurring in any agents. In addition, the accuracy of the observer and the performance of the fault detection mechanism are improved by introducing the disturbance compensation mechanism. Finally, simulation results are provided to illustrate the effectiveness and advantages of the proposed strategy.

An Energy Aware Intelligent Fault Detection Scheme for IoT-Enabled WSNs

Recent years have seen an explosion in the demand for the Internet of Things (IoT). IoT makes any physical object smart by providing sensing capability. The physical objects are embedded with sensors that form a network of sensors. However, sensors are vulnerable to faults due to energy depletion, software failures, and hardware failures. The existing fault detection schemes put huge computational overhead on the battery-limited and low computational capacity sensor nodes that are subject to the premature death of the sensor nodes. Also, they suffer from poor fault detection accuracy and huge false alarm rate that significantly reduce the overall performance of the networks. In this paper, an energy-aware intelligent fault detection scheme is proposed for IoT-enabled wireless sensor networks that significantly improve fault detection accuracy and reduce false alarm rate. A novel 3-Tier hard fault detection mechanism is used for detecting hardware unit faults of the sensor nodes. Furthermore, an optimized deep learning mechanism is used for various soft fault detection that prevents premature death of sensor nodes. The paper mathematically analyses the proposed scheme in terms of energy consumption, time complexity, and message complexity. Extensive simulations show the enhanced performance of the proposed scheme compared with the state-of-the-art algorithms in terms of fault detection accuracy, false alarm rate, false-positive rate, energy consumption, and network lifetime.

Design and Analysis of Current Sensor Fault Detection Mechanisms for PMSM Drives Based on Neural Networks

This paper describes a vector-controlled Permanent Magnet Synchronous Motor (PMSM) drive system with the current sensor fault detection mechanism. In general, the control structure is based on the well-known Field Oriented Control (FOC) algorithm. The structure is equipped with an additional algorithm for current sensor fault detection based on a neural network. The presented control structure is able to detect typical current sensor faults, such as lack of signal, intermittent signal, variable gain and signal noise. The application of the NN detector guarantees a faster detection of the sensor fault than classical detectors based on algorithmic methods or logical systems. This work focuses on presenting the methodology of designing detectors and their analysis, based solely on simulation analysis. The simulation results, conducted in the Matlab/Simulink environment, are presented for the above-mentioned faults in phase A and phase B for different speed conditions.

Random Forest Regressor-Based Approach for Detecting Fault Location and Duration in Power Systems.

Power system failures or outages due to short-circuits or “faults” can result in long service interruptions leading to significant socio-economic consequences. It is critical for electrical utilities to quickly ascertain fault characteristics, including location, type, and duration, to reduce the service time of an outage. Existing fault detection mechanisms (relays and digital fault recorders) are slow to communicate the fault characteristics upstream to the substations and control centers for action to be taken quickly. Fortunately, due to availability of high-resolution phasor measurement units (PMUs), more event-driven solutions can be captured in real time. In this paper, we propose a data-driven approach for determining fault characteristics using samples of fault trajectories. A random forest regressor (RFR)-based model is used to detect real-time fault location and its duration simultaneously. This model is based on combining multiple uncorrelated trees with state-of-the-art boosting and aggregating techniques in order to obtain robust generalizations and greater accuracy without overfitting or underfitting. Four cases were studied to evaluate the performance of RFR: 1. Detecting fault location (case 1), 2. Predicting fault duration (case 2), 3. Handling missing data (case 3), and 4. Identifying fault location and length in a real-time streaming environment (case 4). A comparative analysis was conducted between the RFR algorithm and state-of-the-art models, including deep neural network, Hoeffding tree, neural network, support vector machine, decision tree, naive Bayesian, and K-nearest neighborhood. Experiments revealed that RFR consistently outperformed the other models in detection accuracy, prediction error, and processing time.

Adaptive bipartite output consensus of nonlinear fractional-order multi-agent systems

A distributed control approach is proposed for a class of fractional-order multi-agent systems with unknown nonlinearities under signed bipartite digraphs. Unlike the existing results, the follower's dynamics are studied with strict-feedback fractional-order form as well as a general class of actuator faults with unknown magnitude, pattern and occurrence time is studied. To provide a simple and efficient control strategy, first a novel command fractional-order filter based on the backstepping design is proposed such that the difficulty of calculation of the fractional derivative order of the virtual control laws is removed, as well as improving the bipartite output consensus accuracy. Then, to cope with dynamic's uncertainties, the proposed method is integrated with adaptive neural approximator and minimal learning parameter scheme which reduces communication loads. Besides, a distributed fault compensation protocol based upon the proposed command fractional-order filter and relative output information of neighbours' agents is extended to ensure bipartite output consensus, without relying on any global information of the singed digraph as well as any explicit fault detection mechanism. Finally, it is guaranteed that all the error signals within the closed-loop network system are converged into adjustable compact sets around the origin. The simulation results verify the validity of the presented control approach.

Universal System for Detection and Compensation of Current Sensor Faults in Three-Phase Power Electronic Systems

Abstract The article discusses the universal current sensor fault detection and compensation mechanism, which can be applied in three-phase power electronics (PE) symmetrical system. The mechanism is based on the assumption that a symmetrical system can be described using different components in the stationary reference frame. The solution given in article as a Cri-base detector was tested in electrical drives with induction motors (IMs) and permanent magnet synchronous motors (PMSMs). This study also proves that the same algorithm can work stable in active rectifier systems. Such an application of this detector has not been previously reported in the literature. The article describes the detection of various types of faults in different phases. The fault-tolerant voltage-oriented control (FTVOC) of an active rectifier is compared with previously described solutions for IMs and PMSMs. By analysing in various types of systems, the work proves the universality of the detector based on Cri markers.

Fuzzy Logic and Modified Butterfly Optimization with Efficient Fault Detection and Recovery Mechanisms for Secured Fault-Tolerant Routing in Wireless Sensor Networks

Fuzzy Logic and Modified Butterfly Optimization with Efficient Fault Detection and Recovery Mechanisms for Secured Fault-Tolerant Routing in Wireless Sensor Networks

Sensor fault detection and minimum detectable fault analysis for dynamic point-the-bit rotary steerable system

In this paper, the sensor fault detection problem considering the drilling disturbances is studied for the dynamic point-the-bit rotary steerable system. Firstly, the DPRSS is modeled as a linear system with the drilling disturbances, including unknown inputs, measurement noises, and model perturbations. Then, a finite-frequency zonotopic fault detection observer is proposed. The finite-frequency range H− performance and the P-radius criterion are considered to design the observer gains such that the residuals are sensitive to sensor faults and robust against the drilling disturbances simultaneously. Subsequently, the calculation method of minimum detectable faults is presented for the proposed sensor fault detection mechanism. Finally, simulations and experiments are presented to illustrate the effectiveness of the proposed methods.

Adaptive neural-based finite-time attitude synchronization and tracking control of multiple rigid bodies under actuator faults and saturation

PurposeThe purpose of this paper, is to solve the problem of finite-time fault-tolerant attitude synchronization and tracking control of multiple rigid bodies in presence of model uncertainty, external disturbances, actuator faults and saturation. It is assumed that the rigid bodies in the formation may encounter loss of effectiveness and/or bias actuator faults.Design/methodology/approachFor the purpose, adaptive terminal sliding mode control and neural network structure are used, and a new sliding surface is proposed to guarantee known finite-time convergence not only at the reaching phase but also on the sliding surface. The sliding surface is then modified using a proposed auxiliary system to maintain stability under actuator saturation.FindingsAssuming that the communication topology between the rigid bodies is governed by an undirected connected graph and the upper bounds on the actuators’ faults, estimation error of model uncertainty and external disturbance are unknown, not only the attitudes of the rigid bodies in the formation are synchronized but also they track the time-varying attitude of a virtual leader. Using Lyapunov stability approach, finite-time stability of the proposed control algorithms demonstrated on the sliding phase as well as the reaching phase. The effectiveness of the proposed algorithm is also validated by simulation.Originality/valueThe proposed controller has the advantage that the need for any fault detection and diagnosis mechanism and the upper bounds information on estimation error and external disturbance is eliminated.

Rapid Detection of Small Faults and Oscillations in Synchronous Generator Systems Using GMDH Neural Networks and High-Gain Observers

This paper presents a robust and efficient fault detection and diagnosis framework for handling small faults and oscillations in synchronous generator (SG) systems. The proposed framework utilizes the Brunovsky form representation of nonlinear systems to mathematically formulate the fault detection problem. A differential flatness model of SG systems is provided to meet the conditions of the Brunovsky form representation. A combination of high-gain observer and group method of data handling neural network is employed to estimate the trajectory of the system and to learn/approximate the fault- and uncertainty-associated functions. The fault detection mechanism is developed based on the output residual generation and monitoring so that any unfavorable oscillation and/or fault occurrence can be detected rapidly. Accordingly, an average L1-norm criterion is proposed for rapid decision making in faulty situations. The performance of the proposed framework is investigated for two benchmark scenarios which are actuation fault and fault impact on system dynamics. The simulation results demonstrate the capacity and effectiveness of the proposed solution for rapid fault detection and diagnosis in SG systems in practice, and thus enhancing service maintenance, protection, and life cycle of SGs.

Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review

Electric motors are used extensively in numerous industries, and their failure can result not only in machine damage but also a slew of other issues, such as financial loss, injuries, etc. As a result, there is a significant scope to use robust fault diagnosis technology. In recent years, interesting research results on fault diagnosis for electric motors have been documented. Deep learning in the fault detection of electric equipment has shown comparatively better results than traditional approaches because of its more powerful and sophisticated feature extraction capabilities. This paper covers four traditional types of deep learning models: deep belief networks (DBN), autoencoders (AE), convolutional neural networks (CNN), and recurrent neural networks (RNN), and highlights their use in detecting faults of electric motors. Finally, the issues and obstacles that deep learning encounters in the fault detection mechanism as well as the prospects are discussed and summarized.

Fault-Tolerant FPGA-Based Nanosatellite Balancing High-Performance and Safety for Cryptography Application

With the growth of the nano-satellites market, the usage of commercial off-the-shelf FPGAs for payload applications is also increasing. Due to the fact that these commercial devices are not radiation-tolerant, it is necessary to enhance them with fault mitigation mechanisms against Single Event Upsets (SEU). Several mechanisms such as memory scrubbing, triple modular redundancy (TMR) and Dynamic and Partial Reconfiguration (DPR), can help to detect, isolate and recover from SEU faults. In this paper, we introduce a dynamically reconfigurable platform equipped with configuration memory scrubbing and TMR mechanisms. We study their impacts when combined with DPR, providing three different execution modes: low-power, safe and high-performance mode. The fault detection mechanism permits the system to measure the radiation level and to estimate the risk of future faults. This enables the possibility of dynamically selecting the appropriate execution mode in order to adopt the best trade-off between performance and reliability. The relevance of the platform is demonstrated in a nano-satellite cryptographic application running on a Zynq UltraScale+ MPSoC device. A fault injection campaign has been performed to evaluate the impact of faulty configuration bits and to assess the efficiency of the proposed mitigation and the overall system reliability.

An intelligent fault‐tolerant routing scheme for Internet of Things‐enabled wireless sensor networks

SummaryWireless sensor networks (WSNs) are one of the nonnegligible ingredients in any Internet of Things (IoT)‐based system. Fault tolerance in the IoT‐enabled WSNs is an essential part of avoiding harmful actions. Many approaches are reported in the literature on fault detection and fault tolerance for WSNs. However, they either consume additional energy to identify and tolerate failures or use extra hardware and software resources. This paper proposes an intelligent fault tolerance routing scheme for IoT‐enabled WSNs that significantly improves the QoS of the IoT‐enabled WSNs. We propose a novel two‐population z‐test‐based fault detection mechanism to detect faulty nodes in the networks. Furthermore, an intelligent routing scheme is proposed that reuses different partially faulty nodes to tolerate faults within the network. An extensive experiment on the proposed intelligent fault tolerance routing scheme is performed to demonstrate the efficiency of the proposed scheme. Experiment results are compared with the state‐of‐the‐art algorithms to show the effectiveness of the proposed scheme in terms of false‐positive rate, false alarm rate, fault detection accuracy, energy consumption, fault detection accuracy, and network lifetime.

Correction adaptive square-root cubature Kalman filter with application to autonomous vehicle target tracking

For a state estimation problem of nonlinear system, the square-root cubature Kalman filter (SCKF) is an effective method when the noise statistical characteristics are known. However, the performance of SCKF often degrade significantly in the face of uncertain noises interference, particularly in case of measurement or system failure. In this paper, we focus on improving the accuracy and robustness of SCKF under irregular noise. First, a weighted adaptive SCKF (WASCKF) algorithm is presented with moving window method. The WASCKF can improve the accuracy of SCKF by adaptively adjusting the covariances of measurement noise and process noise. Next, in order to further improve the robustness of WASCKF against the abrupt abnormal noise, a correction adaptive SCKF (CASCKF) algorithm based on fault detection mechanism is proposed. The CASCKF algorithm can detect whether there is a fault according to a statistical function of Chi-square distribution, and can judge and carry out the necessary correction processing by using an isolate rule. Finally, the performance of CASCKF is verified by numerical experiments of autonomous vehicle target tracking problem. The results show that the proposed CASCKF algorithm has good accuracy and robustness even with sudden abnormal noise interference.

Soft Sensing for On-Line Fault Detection of Ammonium Sensors in Water Resource Recovery Facilities.

The increasing demand for online sensors applied to advanced control strategies in water resource recovery facilities has resulted in the increasing investigation of fault-detection methods to improve the reliability of sensors installed in harsh environments. The study herein focuses on the fault detection of ammonium sensors, especially for effluent monitoring, given their potential in ammonium-based aeration control applications. An artificial neural network model was built to predict the ammonium content in the effluent by employing the information from five other sensors installed in the activated sludge tank: NH4+, pH, ORP, DO, and TSS. The residual between the model prediction and the effluent ammonium sensor signal was utilized in a fault-detection mechanism based on principal component analysis and Shewhart monitoring charts. In contrast to previous studies, the present work utilizes typical faults collected from a 1 year historic dataset of an actual sensor setup. Treatment process anomalies, calibration bias faults, and fouling drifts were the most common issues identified from the historic dataset, and they were promptly identified by the proposed fault-detection methodology. Once a fault is detected, the model prediction can be actively used in place of the sensor for process control without affecting the treatment process by utilizing faulty datasets.

Fault-Tolerant Control of a Nonlinear Uncertain System: A Neural Network-Based Passive Approaches and Comparative Study with State-of-the-Art Control Approaches

This article suggests passive methods for designing Fault-Tolerant Control (FTC) for nonlinear uncertain systems with actuator and leak faults. To anticipate the Fault-Tolerant Control (FTC) action to overcome the actuator and leak faults, two-layer Feed-Forward Back-Propagation Neural Network (FFBPNN) and two-layer Cascade Forward Neural Network (CFNN) have been used, it will also tolerate external process additive disturbances. We employ the passive approach for fault-tolerant control using Proportional Integral Derivative (PID) control methodology to create a fault-tolerant controller without a fault detection mechanism. Further, we use the four residue signal features (i.e., mean, variance, skewness and normalize data of residue signal) to train the neural network in this study to tackle the issue originating from having less faults and uncertainty from residue signal. To show the efficacy of the suggested approach, simulations are run. The measurement of the residue signal was done using a healthy and a faulty uncertain non-linear system model. A comparison of findings utilizing a state of-the-art control methodology provided in (Dutta et al., 2014) was also presented to validate the proposed FTC methodology.